TY - GEN
T1 - Mapping growth patterns and genetic influences on early brain development in twins
AU - Chen, Yasheng
AU - Zhu, Hongtu
AU - Shen, Dinggang
AU - An, Hongyu
AU - Gilmore, John
AU - Lin, Weili
PY - 2009
Y1 - 2009
N2 - Despite substantial progress in understanding the anatomical and functional development of the human brain, little is known on the spatial-temporal patterns and genetic influences on white matter maturation in twins. Neuroimaging data acquired from longitudinal twin studies provide a unique platform for scientists to investigate such issues. However, the interpretation of neuroimaging data from longitudinal twin studies is hindered by the lacking of appropriate image processing and statistical tools. In this study, we developed a statistical framework for analyzing longitudinal twin neuroimaging data, which is consisted of generalized estimating equation (GEE2) and a test procedure. The GEE2 method can jointly model imaging measures with genetic effect, environmental effect, and behavioral and clinical variables. The score test statistic is used to test linear hypothesis such as the association between brain structure and function with the covariates of interest. A resampling method is used to control the family-wise error rate to adjust for multiple comparisons. With diffusion tensor imaging (DTI), we demonstrate the application of our statistical methods in quantifying the spatiotemporal white matter maturation patterns and in detecting the genetic effects in a longitudinal neonatal twin study. The proposed approach can be easily applied to longitudinal twin data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.
AB - Despite substantial progress in understanding the anatomical and functional development of the human brain, little is known on the spatial-temporal patterns and genetic influences on white matter maturation in twins. Neuroimaging data acquired from longitudinal twin studies provide a unique platform for scientists to investigate such issues. However, the interpretation of neuroimaging data from longitudinal twin studies is hindered by the lacking of appropriate image processing and statistical tools. In this study, we developed a statistical framework for analyzing longitudinal twin neuroimaging data, which is consisted of generalized estimating equation (GEE2) and a test procedure. The GEE2 method can jointly model imaging measures with genetic effect, environmental effect, and behavioral and clinical variables. The score test statistic is used to test linear hypothesis such as the association between brain structure and function with the covariates of interest. A resampling method is used to control the family-wise error rate to adjust for multiple comparisons. With diffusion tensor imaging (DTI), we demonstrate the application of our statistical methods in quantifying the spatiotemporal white matter maturation patterns and in detecting the genetic effects in a longitudinal neonatal twin study. The proposed approach can be easily applied to longitudinal twin data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.
UR - http://www.scopus.com/inward/record.url?scp=79955665255&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04271-3_29
DO - 10.1007/978-3-642-04271-3_29
M3 - Conference contribution
C2 - 20426117
AN - SCOPUS:79955665255
SN - 3642042708
SN - 9783642042706
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 232
EP - 239
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI2009 - 12th International Conference, Proceedings
T2 - 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
Y2 - 20 September 2009 through 24 September 2009
ER -